Data Science Lead

About the project (description, duration, stage) Hands-on Data Science Lead on a new engagement with a regulated UK & Ireland credit and lending company . The client has consolidated data from multiple business entities into a newly centralized, anonymized data lake and wants to turn it into validated risk analytics — delinquency, probability of default, credit-policy insight — plus an executive-facing natural-language insight layer . This is a foundational data-science build, not an agentic-AI project . The early work is unglamorous and hands-on: validating data nobody can yet vouch for, then building defensible models on top. You are the senior data scientist the client is missing — you do the work and own the methodology , while leading a small pod and acting as the human-in-the-loop the client explicitly asked for. Stage : pre-contract / scoping (Phase 1 = current-state assessment + data validation). Duration : multi-phase, multi-quarter ambition with strong extension probability. Reporting : Engagement lead / CTO (@Alex Honchar); leads the pod's Data Engineer(s) and the client's offshore data team. Full-time engagement is preferable. What you'll actually do (example tasks) Profile the anonymized lake hands-on — interrogate tens-of-millions-of-row tables and reproduce and validate the team's existing descriptive statistics , so every number is traceable to source (the client cannot currently answer “how do you know that's correct?” ). Build and validate the core risk models yourself: PD, delinquency / roll-rate, early-warning, segmentation and scorecards (WOE / IV, logistic regression, gradient boosting). Stand up the model-validation discipline that makes outputs audit-defensible: train / test / out-of-time splits, Gini / AUC / KS, calibration, stability (PSI), backtesting and full model documentation. Define feature logic with the Data Engineer and write it yourself in SQL / dbt / Python ; specify the harmonized definitions the semantic layer must serve. Prototype and validate the natural-language insight layer (text-to-SQL / RAG over the semantic layer); check answer correctness and add guardrails. Run a credit-policy / cut-off analysis showing where the client could tighten policy or reduce delinquency — the concrete insight their own clients keep asking for. Lead a small pod (Data Engineer, client's junior offshore data people): set tasks, review work, be the quality bar and the human-in-the-loop. Front the client's data leadership: present findings, explain methodology to non-technical executives, and shape the phased roadmap / SoW. Skills (hands-on first) Expert Python for data science (pandas / Polars, scikit-learn, statsmodels) and strong SQL over large tables Credit-risk / financial modeling : scorecards, PD, delinquency, segmentation, model validation and governance Data validation, profiling and feature engineering on messy enterprise data dbt / semantic modeling ; partnering with data engineering on the harmonization layer GenAI insight layer: text-to-SQL, RAG over structured data, evaluation and guardrails Methodology, lineage and documentation that survives audit; able to explain it to executives Leadership of small delivery pods and distributed / offshore teams Knowledge GDPR fundamentals (anonymization vs pseudonymization, UK / EU data residency) AWS analytics stack and Well-Architected (Analytics, Security) for BFSI UK / EU credit & lending regulatory context (FCA, model governance, fair-lending / explainability) — strong plus Familiarity with credit-bureau / scoring data products — strong plus Experience Key characteristics (ideally 4/4): Hands-on data science at enterprise scale Worked with financial-services / credit clients or in-house at a credit / lending company Cloud hyperscaler experience (AWS preferred) Technology consulting / client-facing delivery background Role-specific characteristics: 7+ years hands-on data science, with real credit-risk / financial modeling Experience building and validating models in a regulated, audited context Led small data-science teams while still coding personally Demonstrably comfortable doing the data-cleaning grunt work themselves, not just directing it